# models for income sources with livestock cultivation (proxied by pasture lands)
library(specr)
library(fixest)
library(readr)
library(broom.mixed)
library(texreg)
library(dplyr)
library(fixest)


# Cross sectional linear FE models that use aggregate data (2000-2019)

source("funs_and_constants.R")

# load in data
epr_yearly_group_clusters_h1 <- read_csv("data/epro_data_h1.csv")

mean_h1 <- epr_yearly_group_clusters_h1 %>% 
  filter(year >= 2000) %>%# only obs after 2000 because of EPR 2.0 coding
  group_by(gwgroupid, countries_gwid) %>%
  summarize(
    across(c(share_disagreement, disagreement, n_orgs, resource_and_pasture, regaut, groupsize, incidence_flag, status_pwrrank, any_multiethnic,
             nightlight_total_pc_log, n_tek_groups),
           ~ mean(.x, na.rm = T)),
    none_one_or_both_nats_cattle_char = Mode(as.factor(none_one_or_both_nats_cattle_char), na.rm = TRUE),
    disagreement_min = min(disagreement, na.rm = T)
  )


full_model_h1 <- feols(disagreement ~ resource_and_pasture + n_orgs + regaut + groupsize + incidence_flag + status_pwrrank + any_multiethnic + nightlight_total_pc_log + n_tek_groups | countries_gwid,
                       cluster = ~countries_gwid, data = mean_h1 %>% as.data.frame())

full_model_h1_max <- feols(share_disagreement ~ resource_and_pasture + n_orgs + regaut + groupsize + incidence_flag + status_pwrrank + any_multiethnic + nightlight_total_pc_log + n_tek_groups | countries_gwid,
                           cluster = ~countries_gwid, data = mean_h1)

full_model_h1_n_resources <- feols(disagreement ~ none_one_or_both_nats_cattle_char + n_orgs + regaut + groupsize + incidence_flag + status_pwrrank + any_multiethnic + nightlight_total_pc_log + n_tek_groups | countries_gwid,
                                   cluster = ~countries_gwid, data = mean_h1)

full_model_h1_n_resources_max <- feols(share_disagreement ~ none_one_or_both_nats_cattle_char + n_orgs + regaut + groupsize + incidence_flag + status_pwrrank + any_multiethnic + nightlight_total_pc_log + n_tek_groups |
                                         countries_gwid,
                                       cluster = ~countries_gwid, data = mean_h1)


screenreg(list(full_model_h1, full_model_h1_max, full_model_h1_n_resources, full_model_h1_n_resources_max),
          stars = stars)

texreg(list(full_model_h1, full_model_h1_n_resources, full_model_h1_max, full_model_h1_n_resources_max),
       stars = stars,
       custom.coef.map = coef_map,
       custom.header = list("Disagreement (1/0)" = 1:2, "Prop. Disagreement" = 3:4),
       table = FALSE,
       include.proj.stats = FALSE,
       file = "tables/h1_pasture_robustness.tex")



